A line of research in image-based robot manipulation learning relevant to our work involves the use of fully convolutional networks (FCNs). These works take advantage of dense, per-pixel calculations of convolutions and their robustness toward translation shifts of the input. Given an image of the workspace (often in top down view), the FCN outputs a dense, pixelwise action-value map, in which each output pixel corresponds to an input pixel in the same location, which in turn corresponds to a specific location in the workspace. Action-values typically represent the probability of task success in supervised learning or Q-values in reinforcement learning, and usually the action with the highest value is selected in evaluation. This approach has been used in works involving picking [1]}, [2]}, pushing [3]}, throwing [4]}, placing [5]}, various other manipulation actions [6]}, [7]}, navigation [8]}, [9]}, and language instruction [10]}. Our work follows this line with a focus on tool use.
